liteai-ar is an open-weight model from Yassine. It has 494M parameters and a 32,768-token context. At 16-bit it needs about 1.2 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
Runs On
What it takes to serve liteai-ar (494M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 1.0 GB | 1.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.5 GB | 0.6 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.2 GB | 0.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 19, 2026.
liteai-ar on every accelerator the SAVRN Index prices, at every precision
Model Card
The publisher has not written a card for this model.
Configuration
- Architecture
- Qwen2ForCausalLM
- Context length (tokens)
- 32,768
- Layers
- 24
- Hidden size
- 896
- Feed-forward size
- 4,864
- Attention heads
- 14
- Key/value heads
- 2
- Vocabulary size
- 151,936
- Sliding window (tokens)
- 32,768
- RoPE base
- 1e+06
- Stored precision
- float16
- Model type
- qwen2
Identity and Version
- Repository
- Kenvenx/liteai-ar
- Publisher
- Yassine
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 494M parameters
- Languages
- Not stated by the source
- Revision
- e25ad1341a9179c7a02cefed55622bc10b043bc2
- First published
- 2026-09-27
- Last updated
- 2026-09-27
Files and Weights
10 files, 1.0 GB in total. The weights are 1 file totalling 988.1 MB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 988.1 MB | 918d70f51ef2 |
| added_tokens.json | Configuration | 605 B | — |
| config.json | Configuration | 731 B | — |
| generation_config.json | Configuration | 242 B | — |
| special_tokens_map.json | Configuration | 613 B | — |
| .gitattributes | Repository | 1.6 KB | — |
| merges.txt | Tokenizer | 1.7 MB | — |
| tokenizer.json | Tokenizer | 11.4 MB | 9c5ae00e602b |
| tokenizer_config.json | Tokenizer | 7.3 KB | — |
| vocab.json | Tokenizer | 2.8 MB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 988.1 MB
Released by Yassine through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 988.1 MB |
| 16-bit | 1.0 GB |
| 8-bit | 0.5 GB |
| 4-bit | 0.2 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About liteai-ar
How much GPU memory does liteai-ar need?
About 1.2 GB at 16-bit and 0.3 GB at 4-bit: the weights (494M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run liteai-ar on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
What is liteai-ar's context length?
32,768 tokens, from the maximum position embeddings in its published configuration.